Robust TS-ANFIS MPC of an Autonomous Racing Electrical Vehicle Considering the Battery State of Charge
| Autor: | Sergio E. Samada, Vicenç Puig, Fatiha Nejjari |
|---|---|
| Přispěvatelé: | Universitat Politècnica de Catalunya. Doctorat en Automàtica, Robòtica i Visió, Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control |
| Rok vydání: | 2023 |
| Předmět: |
Racing electrical vehicle
Informàtica::Automàtica i control [Àrees temàtiques de la UPC] Electric vehicles Control predictiu Vehicles autònoms Tube-based model predictive control (MPC) Data-driven Takagi–Sugeno (TS) fuzzy model Kalman Filtratge de Computer Science Applications Vehicles elèctrics Trajectory tracking Control and Systems Engineering Zonotopes Adaptive neuro-fuzzy inference system (ANFIS) Predictive control Electrical and Electronic Engineering Kalman filtering Automated vehicles |
| Zdroj: | IEEE/ASME Transactions on Mechatronics. 28:656-667 |
| ISSN: | 1941-014X 1083-4435 |
| DOI: | 10.1109/tmech.2023.3235906 |
| Popis: | © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. In this work, the trajectory tracking problem of an autonomous racing electrical vehicle is addressed. Accordingly, a two-layer control scheme is developed, such that stability, recursive feasibility, and constraint satisfaction are guaranteed. The outer layer includes a zonotopic tube-based predictive control to ensure trajectory tracking while minimizing energy consumption considering the state of charge of the vehicle’s battery. Meanwhile, the inner layer combines a linear quadratic zonotopic controller with a zonotopic Kalman filter to reduce the effect of exogenous disturbances and modeling errors. Moreover, for control and estimation purposes, a data-driven Takagi–Sugeno (TS) model trained by an adaptive neuro-fuzzy inference system (ANFIS) is employed. To illustrate the performance of the proposed control scheme, a simulated 1/10 Scale RC car is used. This work was in part by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERFD) through the project SaCoAV (ref. MINECO PID2020- 114244RB-I00), in part by the European Regional Development Fund of the European Union in the framework of the ERDF Operational Program of Catalonia 2014-2020 (ref. 001-P-001643 Looming Factory), and in part by the DGR of Generalitat de Catalunya (SAC group ref. 2017/SGR/482), and in part by the FI AGAUR under Grant 2021FI-B1 00097. |
| Databáze: | OpenAIRE |
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